DOSEN PROFIL LENGKAP

An AI wrapper (or LLM Wrapper) is commonly defined as either:

in a technical sense, a software layer that sits between the user and an AI model, or
in a broader sense, the services or applications themselves that are available or distributed to businesses or private users using that (or at-least largely basing) on that software layer

Purposes

making AI more usable and effective for specific tasks.
- Instead of having users interact directly with an AI model's raw API or its official website, wrappers handle the Formatting of user inputs, structuring AI outputs in a useful way, enhancing AI capabilities with additional logic or integrations (pipeline?)

example: company-internal AI-powered assistant instead of generic AI chatbot:

Uses proprietary data to provide more accurate responses, (context, pre-prompting, custom GPTs?)
Integrates with internal tools like CRMs and knowledge bases, (agentic AI?)
Follows specific workflows tailored to business needs (propromted)

Functionality

API

Many Applications Are Based on Advanced Prompting
Either Retrieval-augmented generation or Fine-tuning

Example

Criticism

Limitations and Security Concerns

Because of their functionality of generating text, based on probability, LLMs and wrappers thereof are prone to inaccurate responses

Security Concerns

https://shieldbase.ai/blog/innovation-beyond-llm-wrapper

Scalability and Maintenance

The accuracy and performance of AI-powered results worsen, when the volume of queries or complexity of use cases increase. Also, frequent updates to the ground-lying models can break existing wrappers, which can lead to maintenance problems.

Domain-Specific Accuracy

Clankers perform bad in specialized industries where domain knowledge is critical (healthcare, finance, or legal). Fine-tuning alone may not be sufficient.

Ethical and Bias Concerns

Pre-trained models, can inherit biases present in their training data.

Prominent Examples

Perplexity AI, a AI search engine uses large language models and real-time web search capabilities, to provide responses based on current Internet content, citing sources used. Its real-time search engine is called Sonar and is based on Meta's open-source Llama. A free public version is available, while a paid Pro subscription offers access to more advanced language models and additional features.

https://thenewstack.io/more-than-an-openai-wrapper-perplexity-pivots-to-open-source/

https://analyticsindiamag.com/global-tech/perplexity-ai-is-destroying-the-llm-wrapper-myth

References